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CellScore

This is the development version of CellScore; for the stable release version, see CellScore.

Tool for Evaluation of Cell Identity from Transcription Profiles


Bioconductor version: Development (3.19)

The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized.

Author: Nancy Mah [aut, cre], Katerina Taskova [aut], Justin Marsh [aut]

Maintainer: Nancy Mah <nancy.l.mah at googlemail.com>

Citation (from within R, enter citation("CellScore")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("CellScore")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

Reference Manual PDF

Details

biocViews DataImport, GeneExpression, Microarray, MultipleComparison, ReportWriting, Software, Transcription, Visualization
Version 1.23.0
In Bioconductor since BioC 3.7 (R-3.5) (6 years)
License GPL-3
Depends R (>= 4.3.0)
Imports Biobase(>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer (>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils (>= 3.5.0), SummarizedExperiment
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Suggests hgu133plus2CellScore, knitr, testthat (>= 3.0.0)
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package
Windows Binary
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/CellScore
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/CellScore
Package Short Url https://bioconductor.org/packages/CellScore/
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